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Analysis on Text Compression Algorithms

Analysis on Text Compression Algorithms

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： P Aruna L Yamuna Devi Danusri E Pragathy V Y Sri Jaya Vaishnavi K S Department of Computing Coimbatore Institute of Technology Coimbatore India Department of Computer Applications Coimbatore Institute of Technology Coimbatore India Department of Computing (SS) Coimbatore Institute of Technology Coimbatore India

In the rapidly evolving digital age, the exponential growth of data accumulation has become a ubiquitous challenge, necessitating efficient file compression techniques for optimized data handling, transmission, and storage. The fundamental concepts and advancements in file compression are explored, shedding light on its significance in today’s data-driven world, especially in the field of cloud computing. Cloud computing demands resource efficiency and cost-effectiveness, making compression crucial for data management. Among these techniques, Huffman Coding (Statistical Algorithm) and LZ77 (Dictionary-Based Algorithm) are two prominent algorithms that have found widespread applications in various fields, ranging from data storage and communication to multimedia compression. Additionally, the combination of both Huffman Coding and LZ77 (Deflate Algorithm) are examined, which explores the hybrid compression techniques that harness the strengths of both algorithms. Overall, a comprehensive overview of file compression techniques and their applications are provided, making it a valuable resource for professionals and researchers seeking to optimize data management in the face of mounting data volumes and cloud-based demands.

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System for Predicting Heart Disease using Sophisticated Machine Learning Techniques

System for Predicting Heart Disease using Sophisticated Mach...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Bhusan Dash, Bibhuti Barik, Lalbihari De, Utpal Chandra Tripathy, Abinash Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Kiit Deemed to Be University School of Computer Applications Odisha Bhubaneswar India

ISBN: (纸本)9798350308167

Cardiovascular illness stands as a significant global health issue, and timely identification of this condition holds paramount importance to ensure effective intervention. In this paper, we introduce a fused model for anticipating heart ailments. This amalgamation incorporates four distinct algorithms: LightGBM, XGBoost, Multilayer Perceptron, and AdaBoost. Through an assessment on the Cleveland dataset, AdaBoost emerged as the most accurate with a precision of 0.985. Subsequently, XGBoost followed closely with an accuracy of 0.980, succeeded by LightGBM at 0.956, and Multilayer Perceptron at 0.936. To harness the collective power of these models, we employed a weighted bagging technique that accords weights to predictions based on their individual accuracy rates. The resultant composite model attained an accuracy of 0.96. This outcome is slightly lower than the model with the highest accuracy, as it involves a weighted mean calculation across model predictions. This study indicates the potential utility of the proposed methodology in cardiac disease prediction, leveraging the ensemble approach to potentially outperform standalone models. © 2023 IEEE.

关键词： AdaBoost Ensemble model Heart Disease LightGBM Machine Learning Multilayer Perceptron XGBoost

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Energy-Saving VM in SDN-Driven Cloud Data Center with the Operational Vacation Policy 3

Energy-Saving VM in SDN-Driven Cloud Data Center with the Op...

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3rd International Conference on Innovative Mechanisms for Industry applications, ICIMIA 2023

作者： Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Barik, Lalbihari Ghosh, Akash Pandey, Trilok Nath Gourisaria, Mahendra Kumar KIIT Deemed to Be University School of Computer Applications Bhubaneswar India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The increasing use of the Internet of Things (IoT), which has resulted in an exponential growth in network traffic, has serious implications on energy consumption and network performance. To reduce power consumption in cloud data centers, a server vacation-based energy-efficient traffic management solution is recommended. The primary idea is that using software-defined network (SDN) technologies, cloud system virtual machines (VMs) can take vacations. The goal of the energy-saving optimization task is to reduce the number of virtual machines (VMs) in use while maintaining service quality standards. This research study focuses on impatient client behaviour, several vacations, limited buffer and queuing system with many servers. Impatient customers may be kept in the cloud system by implementing certain customer retention strategies. The steady-state probability for the proposed SDN-assisted cloud system have been determined using recurrences. To determine the efficiency of the proposed system, several performance studies are conducted. © 2023 IEEE.

关键词： Cloud data center Energy optimization Performance measurement Queueing model Software Defined Network Vacation policy

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Artificial Intelligence in Adherence of Climate Change Estimation: Opportunities and Challenges 4

Artificial Intelligence in Adherence of Climate Change Estim...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Duklan, Nitin Ghildiyal, Sunil Maheshwari, Himani Joshi, Kapil Kumar, Santosh Umang Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India Graphic Era Hill University School of Computing Department of Computing Uttarakhand Dehradun India Sharda University Department of Mathematics Sharda School of Basic Sciences and Research Greater Noida India Kumaun University D.S.B. Campus Department of Computer Applications Nainital India

ISBN: (纸本)9798350300093

Historical data on structural energy usage, artificial intelligence (AI) is able to produce estimations more precisely and quickly. Forecasting building energy performance, which looks across different construction levels, may assist city & neighborhood leaders in better anticipating their future energy demands and also in making optimal plans for addressing them. This study provides a campus energy consumption prediction tool for anticipating the impacts of long-term climate change upon this energy efficiency of buildings utilizing AI methods, with an emphasis here on urban scale. This study concludes that harnessing the potential of AI for combating climate change worldwide while containing its hazards is a gamble that requires strong, scientific proof governance in order to succeed. In the last section, this study identifies the European Union as being particularly well-positioned to act as a leader in this reform strategy & offer ideas for how to recognise and take advantage of AI's potential for tackling climate change whilst minimising its environmental effect. © 2023 IEEE.

关键词： Climate change

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Advanced Data Segmentation and Encryption Approach using Fog computing

Advanced Data Segmentation and Encryption Approach using Fog...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Singh, Kamred Udham Varshney, Neeraj Kumar, Pushpendra Sharma, Pradeep Kumar Mishra, Umang Deep Singh, Teekam Graphic Era Hill University School of Computing Dehradun India Gla University Department of Computer Engineering & Applications UP Mathura India Scanno System Greater Noida India Hcl Technology UP India Unique Institute of Management & Technology Muradnagar India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350382723

Data security in conventional applications was only available at the application layer. Within the scope of the intended research, the safety of the merchandise is ensured. The deficiencies that are present in the security systems that are already in place have led to the realisation that a new security system is required. Considering that authentication is not present, the likelihood of decryption should be reduced. Establishing a decentralised security system is essential in order to protect against threats coming from a variety of digital networks. The new encryption standard is said to improve multilayer security, according to the claims. The material is split into two halves using this method, which also provides a reliable transmission mechanism. Because of the technology, hackers are unable to create vulnerabilities in the security system. There was a need for security in both the cloud and the edge environment in order to prevent intruders. Increasing the reliability and security of data transmission over a variety of networks was the motivation for the development of the two fish approach. Because of its security mechanism, it has been able to survive assaults from crackers and hackers. The paper that is being presented advises doing research on the safety of edge transmissions that are based on slicing. In order to prevent attackers from launching attacks from several networks, it is necessary to establish decentralised security. Layered security will help increase encryption standards, which is a significant benefit. Over the course of various investigations, computer security edge generators have been the focus of attention. Quite a few of them have already been extensively discussed. For the purpose of developing a reliable method of transmission, the proposed research splits the data into two parts, each of which is further encrypted at a different level. As a result of the development of multilayer encryption, the reliability and safety of data transm

关键词： Fish

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Transmission Line Fault Detection and Classification Using Feature Extraction Based Self-Attention Convolutional Neural Network with Time Series Image 3

Transmission Line Fault Detection and Classification Using F...

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3rd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Almusawi, Muntather Babu, D. Raja Mazumder, Debarshi Bala Dhandayuthapani, V. Saranya, N. Naga College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Sr University Department Of Electrical And Electronics Engineering Warangal India Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India College Of Computing And Information Sciences University Of Technology And Applied Sciences Department Of It Shinas campus Oman Saveetha College Of Liberal Arts & Science Simats Department Of Computer Applications Chennai India

ISBN: (纸本)9798350318609

The transmission lines continuously experience the number of shunt faults and its effective in practical system increases the instability, load damages and line restoration cost. This work implements an advanced self-attention convolutional neural network (SAT-CNN) method for fault detection and classification (FDC) of high voltage transmission lines experiencing a constant number of shunt faults. Faults cause load damage in real-time applications, increase instability, and increase the cost of line restoration. Therefore, a precise model is needs to identify and categorize the flaws to quickly restore he problematic phases. In this research, implemented SAT-CNN feature extraction model with imaging-based on time series that can accurately detect and classify faults. By employing a number of input signals, including, current, voltage, and combined current and voltage signals, at different sampling frequencies, the efficacy of implemented SAT-CNN model is evaluated. Implemented SAT-CNN method obtains high performance when compared to existing methods including weight-sharing network (WSCN), Truncated singular value decomposition and Human urbanization algorithm based Recurrent Perceptron Neural Network (TSVD-HUARPNN), and Graph convolutional neural network (GCN), and result achieved 99.90% accuracy value. © 2024 IEEE.

关键词： Convolutional neural networks

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YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness

YOLOv8: A Novel Object Detection Algorithm with Enhanced Per...

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Advances in Data Engineering and Intelligent computing Systems (ADICS), International Conference on

作者： Rejin Varghese Sambath M. Department of Computer Applications Hindustan Institute of Technology and Science Chennai India School of Computing Science Hindustan Institute of Technology and Science Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

In recent years, the You Only Look Once (YOLO) series of object detection algorithms have garnered significant attention for their speed and accuracy in real-time applications. This paper presents YOLOv8, a novel object detection algorithm that builds upon the advancements of previous iterations, aiming to further enhance performance and robustness. Inspired by the evolution of YOLO architectures from YOLOv1 to YOLOv7, as well as insights from comparative analyses of models like YOLOv5 and YOLOv6, YOLOv8 incorporates key innovations to achieve optimal speed and accuracy. Leveraging attention mechanisms and dynamic convolution, YOLOv8 introduces improvements specifically tailored for small object detection, addressing challenges highlighted in YOLOv7. Additionally, the integration of voice recognition techniques enhances the algorithm's capabilities for video-based object detection, as demonstrated in YOLOv7. The proposed algorithm undergoes rigorous evaluation against state-of-the-art benchmarks, showcasing superior performance in terms of both detection accuracy and computational efficiency. Experimental results on various datasets confirm the effectiveness of YOLOv8 across diverse scenarios, further validating its suitability for real-world applications. This paper contributes to the ongoing advancements in object detection research by presenting YOLOv8 as a versatile and high-performing algorithm, poised to address the evolving needs of computer vision systems.

关键词： YOLO Performance evaluation Technological innovation Computer vision Heuristic algorithms Speech recognition Benchmark testing

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Internet of Things (IoT) applications Using Smart Materials and Sensor Technologies to Prevent COVID-19 4

Internet of Things (IoT) Applications Using Smart Materials ...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Sekhar, Jampani Chanda Angelin, P. Anitha Christy Prasad, R. Giri Uthirapathy, Samson Ebenezar Agrawal, Prashant Suthar, Trilok Nri Institute of Technology Department of Cse Andhra Pradesh Guntur522438 India Psna College of Engineering and Technology Department of Computer Science & Engineering Dindigul624622 India Aditya Engineering College Department of Petroleum Technology Andhra Pradesh Surampalem533437 India School of Computing Science Galgotias University Greater Noida201306 India Kiet Group of Institutions Department of Computer Applications Delhi NCR Ghaziabad201206 India Parul University Department of Information Technology Vadodara391760 India

ISBN: (纸本)9798350387490

In order to effectively screen patients during the COVID-19 pandemic, novel strategies have been required on a global scale. Smart data and the Internet of Things (IoT) have proven to be important in this context. These technologies provide alternatives to traditional diagnostic tests, increase patient satisfaction, and decrease hospital readmissions. By integrating IoT sensor technologies with intelligent materials, they demonstrate analytical capabilities that facilitate the timely diagnosis of COVID-19 symptoms. This study evaluates the feasibility of IoT connectivity in all areas, photosensitive, and electrically sensitive devices for assisting COVID-19 patients effectively by integrating biosensors to monitor symptoms such as fever and respiratory difficulties. By incorporating COVID-19 diagnostics, intelligent materials, biosensors, and viral detection examples, the study demonstrates how IoT frameworks can be implemented in intelligent healthcare. © 2024 IEEE.

关键词： COVID-19

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Advancements in Crown Sheath Rot Detection using DenseNet121 and UNet in Rice Plant Leaf Analysis

Advancements in Crown Sheath Rot Detection using DenseNet121...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thilagavathy, R. Kumar, S.Senthil Deebalakshmi, R. Deepa, D. Kavitha, R. Kumar, L. Sathish Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur India School of Arts and Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirappalli India Kongu Engineering College Department of Computer Science and Engineering Tamil Nadu Perundurai India Karpagam Academy of Higher Education Department of Computer Applications TN Coimbatore India Vit Bhopal University School of Computing Science and Engineering MP Kotrikalan India

ISBN: (纸本)9798350348910

This article aims to investigate the leaf spot disease in rice, a condition that has been observed to cause substantial reductions in crop yield, ranging from 10% to 50%. The disease is characterized by the appearance of circular lesions on the leaves of rice plants, which gradually expand and cover the entire leaf surface, leading to the eventual death of the affected leaves. In order to achieve automation in the detection and severity classification of the disease under investigation, a range of deep learning models were utilized. These models encompassed Faster DenseNet121 and UNet. These methods proposed in this study was trained and tested using a dataset consisting of 1120 images. The findings of the study indicate that the DenseNet121 approach that yielded the highest level of success demonstrated a noteworthy classification accuracy rate of 95.23%. The results obtained from the integration of image processing techniques with deep learning models demonstrated superior performance compared to the analysis solely based on deep learning models. The integration described in this study presents a novel approach to enhance the efficiency of diagnosing leaf spot disease on rice. By leveraging advanced technology, this integration aims to reduce the time required for diagnosis, minimize the occurrence of human errors, and facilitate the timely application of suitable pesticides to mitigate the detrimental effects of the disease. © 2023 IEEE.

关键词： Integration

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Enhancing Traffic Engineering Policy in SDN Addressing TCAM limitations using Bloom Filters 4

Enhancing Traffic Engineering Policy in SDN Addressing TCAM ...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Ghosh, Akash Barik, Lalbihari Rout, Suchismita Rawat, Bhupender Singh Jena, Prakash Chandra Patra, Sudhansu Shekhar School of Computer Applications KIIT Deemed To be University Bhubaneswar India Department of Information Systems Faculty of Computing and Information Technology in Rabigh King Abdul Aziz University Saudi Arabia College of Smart Computing COER University Roorkee India Department of Computer Sc and Engg. Hi-Tech College of Engineering Bhubaneswar India

ISBN: (纸本)9798350305258

With the increasing demand for new technologies, the speed of delivering a packet is becoming the major focus of various industries. The generation of huge traffic is becoming the bottleneck of the current scenario. By segregating the control plane from the data plane, Software Defined Networking (SDN) is able to many such issues to certain extent. But the traffic engineering problem still remains as an important topic to focus on. Ternary Content Addressable Memory (TCAM) is capable of solving many such issues with the cost of its cost and power issues. For the effective use of TCAM, various algorithms and mechanisms have been proposed by many researchers. Bloom filter is one of the popular ways which is able to provide effective solutions after collaborating with TCAM. An algorithm has been proposed which is able to find the exact match of the entry in TCAM. The use of a CRC generator and multiple hashing tables makes it effective at finding the presence of a flow rule in TCAM, in very little time. The major focus is on the fast search procedure for finding the exact match of the flow rule. The number of false positives (FPs) is reduced to a lower percentage value as compared to the existing approaches. © 2023 IEEE.

关键词： Bloom Filter False positive IP addressing multi-hashing Prefix match SDN TCAM Traffic Engineering

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